A Machine Learning Approach for Analyzing and Predicting Suicidal Thoughts and Behaviors

Suicide is a significant public health concern, and there is growing interest in using machine learning techniques to identify people who are at a high risk of committing suicide. In this paper, a review of the current state-of-the-art in suicide prediction is given using machine learning. Various features are investigated with data sources used in earlier studies, such as text-based data from social media, electronic health records, and demographic data. Also, different machine learning techniques are analyzed that are employed including neural networks. We compare the different machine learning models based on errors and find that Support Vector Regression (SVR) to be the most suitable for this purpose. We conclude by emphasizing the potential of machine learning to improve suicide prevention efforts and addressing the ethical concerns that must be discussed when implementing such models in practice.